EEG functional connectivity in infants at elevated familial likelihood for autism spectrum disorder

Background Many studies have reported that autism spectrum disorder (ASD) is associated with atypical structural and functional connectivity. However, we know relatively little about the development of these differences in infancy. Methods We used a high-density electroencephalogram (EEG) dataset pooled from two independent infant sibling cohorts, to characterize such neurodevelopmental deviations during the first years of life. EEG was recorded at 6 and 12 months of age in infants at typical (N = 92) or elevated likelihood for ASD (N = 90), determined by the presence of an older sibling with ASD. We computed the functional connectivity between cortical sources of EEG during video watching using the corrected imaginary part of phase-locking values. Results Our main analysis found no significant association between functional connectivity and ASD, showing only significant effects for age, sex, age-sex interaction, and site. Given these null results, we performed an exploratory analysis and observed, at 12 months, a negative correlation between functional connectivity and ADOS calibrated severity scores for restrictive and repetitive behaviors (RRB). Limitations The small sample of ASD participants inherent to sibling studies limits diagnostic group comparisons. Also, results from our secondary exploratory analysis should be considered only as potential relationships to further explore, given their increased vulnerability to false positives. Conclusions These results are inconclusive concerning an association between EEG functional connectivity and ASD in infancy. Exploratory analyses provided preliminary support for a relationship between RRB and functional connectivity specifically, but these preliminary observations need corroboration on larger samples. Supplementary Information The online version contains supplementary material available at 10.1186/s13229-023-00570-5.


Controlling for sample size bias in the estimation of functional connectivity
Since CIPLV is biased by the sample size (Supplementary Figure 1.a), we computed connectivity using the same number of epochs (Nsel) for each subject.However, this approach presents a challenge since there is a large difference in the number of available epochs (Ntot) between recordings.To avoid rejecting a significant amount of recordings because they do not have enough valid data, we have to use a relatively low value for Nsel: 20 1-s epochs.
Figure 1.b illustrates the tradeoff in choosing the optimal value for Nsel.We used bootstrapping to compute the functional connectivity as the mean value of a set of N estimates based on Nselepoch random subsamples (without replacement), with N taken as twice the value of Ntot divided by Nsel.For example, for a recording with Ntot=100 epochs, this rule results in N=2xNtot/Nsel=2x100/20, or 10 estimates.This way, most epochs (but not all, since the selection is random) are used to obtain the bootstrapped estimates, with epochs being used on average twice.This approach ensured we took advantage of most of the available data while eliminating the bias due to differences in the number of available epochs across subjects, sites, and time points.

Reliability of CIPLV
To illustrate the superior reliability of CIPLV estimates compared to the often-used weighted PLI (wPLI) measure, we bootstrapped the estimation of the CIPLV and wPLI for 100 iterations and computed the mean and the standard deviation of these samples for every pair of channels.

Rejection of channels and independent components
We interpolated channels flagged as containing artifacts using spherical splines, as implemented in MNE-Python.Similarly, we removed from the raw EEG all independent components flagged as not representing neural signals.This procedure is described and validated in detail in [23].Supplementary Figure 3 illustrates the distribution of dropped channels and independent components, split by site, sex, and group.We computed two mixedeffect linear regressions using the formula "Y ~ age + site + sexe + group", with Y taken as the percentage of bad channels and independent components, and with the participant identifier as grouping factor (i.e., random effect).The proportions of bad channels and components were impacted only by the site factor (channel: p=7.7e-19, component: p=1.7e-21; all other p-values > 0.05).Supplementary Figure 3 shows relatively large rejection proportions.This high level of rejection is partly due to the amount of noise present in infant EEG recordings.However, it is also due to a rather conservative inclusion of channels and components.This approach has been shown to be effective in avoiding rejecting more recordings than necessary and increasing the signal-to-noise ratio in EEG data, as illustrated by larger evoke-related potentials than when using alternative pipelines [23].
Supplementary Figure 3.Comparison of the percentage of bad channels and independent components rejected.

Effect of group imbalance on statistical power
Our analyses have been limited by decreased statistical power due to diagnostic group imbalance.To exemplify the effect of an imbalance between groups, we consider a fictive sample of 20 ASD subjects and 200 controls, and we suppose that we are interested in a measure that has a normal distribution with mean values of 1 and 2 for these two groups and the same standard deviation across groups =1.The standard error for these two distributions will be equal to

Additional linear regression
The results of models ( 2) and (3) without averaging connectivity measures within recordings are shown in Supplementary Tables 1 and 2. These models show more significant p-values and even some significant interactions with group and overall ADOS CSS.However, we note the absence of significance for the main effects of group and ADOS, which casts some doubts on the reliability of these interactions.Further, we prefer the more conservative model presented in the main text because of the difficulty of fully capturing the correlation structure between observations in models with large amounts of repeated observations.We consider this complexity to be more likely to lead to misleading results.For example, in this specific case, the connectivity measures establish a mapping between two sets of regions.Thus, these measures are likely to have a complex correlational structure, which would be hard to properly model without increasing significantly the complexity of these models, the number of parameters, and the difficulty of finding reliable parameter estimates.Thus, we believe the model using averaged connectivity to be more conservative and reliable, although it is likely to have less statistical power and more susceptible to false negatives.

Figure 1
Figure 1.c-e shows the distribution of these standard deviations (c), mean (d), and the ratio

Figure 2 .
Stacked histograms illustrating the rejection of statistical outliers.Vertical lines show the median (solid black lines), the first and third quartiles (dashed black lines), and the threshold defined in (4) (dashed red lines).Rows and columns separate the recording site and the age at the time of recording, respectively.
071, respectively.Consequently, the difference of the means will be a normal distribution with mean values =1-2 and a pooled standard error equal to  = 4 235.This standard error is the same as we would obtain with equal samples of 36.4 subjects.That means that our total unbalanced sample of 220 subjects has the same statistical power as a balanced sample of about 72 subjects (i.e., the effective sample size for this study is equal to 72 subjects, not 220).Thus, in our study, we do not benefit much from our larger sample of control subjects for group comparisons with ELA-ASD.We might further note that a longer follow-up might have increased the size of the ASD group, particularly for the Seattle site, since ASD often goes undetected at young ages (e.g., at 2 years old)[61].This limitation is not present for comparison between ELA and TLA since these groups are balanced.The original studies were powered to study such group comparisons.